Batch-level Experience Replay with Review for Continual Learning
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Abstract (arXiv)
Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity. The CVPR 2020 CLVision Continual Learning for Computer Vision challenge is dedicated to evaluating and advancing the current state-of-the-art continual learning methods using the CORe50 dataset with three different continual learning scenarios. This paper presents our approach, called Batch-level Experience Replay with Review, to this challenge. Our team achieved the 1'st place in all three scenarios out of 79 participated teams. The codebase of our implementation is publicly available at https://github.com/RaptorMai/CVPR20_CLVision_challenge
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2020.
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Citing papers per year: 2021: 4; 2022: 0; 2023: 2; 2024: 1; 2025: 3.
10 papers.
- 2021Online Continual Learning in Image Classification: An Empirical Surveycited by 242
- 2021A TinyML Platform for On-Device Continual Learning with Quantized Latent Replayscited by 37
- 2023Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replaycited by 20
- 2024Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognitioncited by 16
- 2021Continual Learning for Monolingual End-to-End Automatic Speech Recognitioncited by 8
- 2025AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Modelscited by 5
- 2023New Insights on Relieving Task-Recency Bias for Online Class Incremental Learningcited by 4
- 2025Experience Replay Addresses Loss of Plasticity in Continual Learningcited by 4
- 2021Learning to Continually Learn Rapidly from Few and Noisy Datacited by 3
- 2025Sample Compression for Self Certified Continual Learningcited by 1
- 2025AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models10 Jun 2025
- 2025Experience Replay Addresses Loss of Plasticity in Continual Learning25 Mar 2025
- 2025Sample Compression for Self Certified Continual Learning13 Mar 2025
- 2024Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition24 Sep 2024
- 2023Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay27 Aug 2023
- 2023New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning16 Feb 2023
- 2021Continual Learning for Monolingual End-to-End Automatic Speech Recognition17 Dec 2021
- 2021A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays20 Oct 2021
- 2021Learning to Continually Learn Rapidly from Few and Noisy Data6 Mar 2021
- 2021Online Continual Learning in Image Classification: An Empirical Survey25 Jan 2021